Overview

Dataset statistics

Number of variables15
Number of observations7022
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory823.0 KiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

TIME is highly correlated with 轉速 and 13 other fieldsHigh correlation
轉速 is highly correlated with TIME and 9 other fieldsHigh correlation
T1 is highly correlated with TIME and 9 other fieldsHigh correlation
T3 is highly correlated with TIME and 8 other fieldsHigh correlation
T4 is highly correlated with TIME and 10 other fieldsHigh correlation
T6 is highly correlated with TIME and 7 other fieldsHigh correlation
T7 is highly correlated with TIME and 8 other fieldsHigh correlation
Z is highly correlated with TIME and 9 other fieldsHigh correlation
T2 is highly correlated with TIME and 7 other fieldsHigh correlation
T5 is highly correlated with TIME and 6 other fieldsHigh correlation
T8 is highly correlated with TIME and 9 other fieldsHigh correlation
T9 is highly correlated with TIMEHigh correlation
T11 is highly correlated with TIMEHigh correlation
T10 is highly correlated with TIME and 2 other fieldsHigh correlation
T12 is highly correlated with TIMEHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
轉速 has 1190 (16.9%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:25:30.412790
Analysis finished2022-11-11 03:25:37.781704
Duration7.37 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7022
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.5416667
Minimum0
Maximum585.0833333
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:37.809611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.25416667
Q1146.2708333
median292.5416667
Q3438.8125
95-th percentile555.8291667
Maximum585.0833333
Range585.0833333
Interquartile range (IQR)292.5416667

Descriptive statistics

Standard deviation168.935094
Coefficient of variation (CV)0.5774736156
Kurtosis-1.2
Mean292.5416667
Median Absolute Deviation (MAD)146.2916667
Skewness6.763576948 × 10-16
Sum2054227.583
Variance28539.06597
MonotonicityStrictly increasing
2022-11-11T11:25:37.868413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
389.751
 
< 0.1%
390.66666671
 
< 0.1%
390.58333331
 
< 0.1%
390.51
 
< 0.1%
390.41666671
 
< 0.1%
390.33333331
 
< 0.1%
390.251
 
< 0.1%
390.16666671
 
< 0.1%
390.08333331
 
< 0.1%
Other values (7012)7012
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
585.08333331
< 0.1%
5851
< 0.1%
584.91666671
< 0.1%
584.83333331
< 0.1%
584.751
< 0.1%
584.66666671
< 0.1%
584.58333331
< 0.1%
584.51
< 0.1%
584.41666671
< 0.1%
584.33333331
< 0.1%

轉速
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12891.19909
Minimum0
Maximum23000
Zeros1190
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:37.921554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19000
median14000
Q319000
95-th percentile23000
Maximum23000
Range23000
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation7178.129876
Coefficient of variation (CV)0.5568240647
Kurtosis-0.6953094512
Mean12891.19909
Median Absolute Deviation (MAD)5000
Skewness-0.5663466708
Sum90522000
Variance51525548.51
MonotonicityNot monotonic
2022-11-11T11:25:37.963757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
01190
16.9%
16000366
 
5.2%
22000366
 
5.2%
21000366
 
5.2%
20000366
 
5.2%
19000366
 
5.2%
18000366
 
5.2%
17000366
 
5.2%
15000366
 
5.2%
13000366
 
5.2%
Other values (7)2538
36.1%
ValueCountFrequency (%)
01190
16.9%
8000360
 
5.1%
9000360
 
5.1%
10000360
 
5.1%
11000366
 
5.2%
12000366
 
5.2%
13000366
 
5.2%
14000360
 
5.1%
15000366
 
5.2%
16000366
 
5.2%
ValueCountFrequency (%)
23000366
5.2%
22000366
5.2%
21000366
5.2%
20000366
5.2%
19000366
5.2%
18000366
5.2%
17000366
5.2%
16000366
5.2%
15000366
5.2%
14000360
5.1%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)0.5%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean26.46603962
Minimum24.9
Maximum28.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.012376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.9
5-th percentile25.4
Q125.9
median26.2
Q327
95-th percentile28.1
Maximum28.5
Range3.6
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.8018327461
Coefficient of variation (CV)0.03029666537
Kurtosis-0.311938268
Mean26.46603962
Median Absolute Deviation (MAD)0.4
Skewness0.6747014521
Sum185712.2
Variance0.6429357527
MonotonicityNot monotonic
2022-11-11T11:25:38.128113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
26694
 
9.9%
25.9604
 
8.6%
26.1557
 
7.9%
26.3348
 
5.0%
26.6300
 
4.3%
26.2300
 
4.3%
25.8294
 
4.2%
26.4270
 
3.8%
26.8267
 
3.8%
27.2254
 
3.6%
Other values (27)3129
44.6%
ValueCountFrequency (%)
24.947
 
0.7%
259
 
0.1%
25.145
 
0.6%
25.266
 
0.9%
25.384
 
1.2%
25.4156
2.2%
25.5236
3.4%
25.6227
3.2%
25.7223
3.2%
25.8294
4.2%
ValueCountFrequency (%)
28.543
 
0.6%
28.498
1.4%
28.348
 
0.7%
28.241
 
0.6%
28.1143
2.0%
28132
1.9%
27.922
 
0.3%
27.8138
2.0%
27.7168
2.4%
27.634
 
0.5%

T2
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.0 KiB
25.0
2843 
25.1
1969 
24.9
1665 
24.8
529 
24.7
 
16

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters28088
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25.0
2nd row25.0
3rd row25.0
4th row25.0
5th row25.0

Common Values

ValueCountFrequency (%)
25.02843
40.5%
25.11969
28.0%
24.91665
23.7%
24.8529
 
7.5%
24.716
 
0.2%

Length

2022-11-11T11:25:38.180899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:38.230080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
25.02843
40.5%
25.11969
28.0%
24.91665
23.7%
24.8529
 
7.5%
24.716
 
0.2%

Most occurring characters

ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
54812
17.1%
02843
10.1%
42210
 
7.9%
11969
 
7.0%
91665
 
5.9%
8529
 
1.9%
716
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21066
75.0%
Other Punctuation7022
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27022
33.3%
54812
22.8%
02843
13.5%
42210
 
10.5%
11969
 
9.3%
91665
 
7.9%
8529
 
2.5%
716
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.7022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
54812
17.1%
02843
10.1%
42210
 
7.9%
11969
 
7.0%
91665
 
5.9%
8529
 
1.9%
716
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII28088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
54812
17.1%
02843
10.1%
42210
 
7.9%
11969
 
7.0%
91665
 
5.9%
8529
 
1.9%
716
 
0.1%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.90687838
Minimum24.4
Maximum25.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.272046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile24.6
Q124.8
median25
Q325
95-th percentile25.1
Maximum25.1
Range0.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1574426074
Coefficient of variation (CV)0.006321250099
Kurtosis-0.01420733693
Mean24.90687838
Median Absolute Deviation (MAD)0.1
Skewness-0.8449037284
Sum174896.1
Variance0.02478817464
MonotonicityNot monotonic
2022-11-11T11:25:38.310565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
252392
34.1%
24.91387
19.8%
25.11138
16.2%
24.8864
 
12.3%
24.7623
 
8.9%
24.6411
 
5.9%
24.5193
 
2.7%
24.414
 
0.2%
ValueCountFrequency (%)
24.414
 
0.2%
24.5193
 
2.7%
24.6411
 
5.9%
24.7623
 
8.9%
24.8864
 
12.3%
24.91387
19.8%
252392
34.1%
25.11138
16.2%
ValueCountFrequency (%)
25.11138
16.2%
252392
34.1%
24.91387
19.8%
24.8864
 
12.3%
24.7623
 
8.9%
24.6411
 
5.9%
24.5193
 
2.7%
24.414
 
0.2%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.52546283
Minimum25.2
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.353472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.2
5-th percentile25.2
Q125.5
median25.5
Q325.6
95-th percentile25.8
Maximum25.9
Range0.7
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.158051826
Coefficient of variation (CV)0.006191927919
Kurtosis-0.08682244865
Mean25.52546283
Median Absolute Deviation (MAD)0.1
Skewness-0.131042284
Sum179239.8
Variance0.02498037969
MonotonicityNot monotonic
2022-11-11T11:25:38.391937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
25.52853
40.6%
25.61112
 
15.8%
25.7851
 
12.1%
25.4583
 
8.3%
25.8577
 
8.2%
25.2526
 
7.5%
25.3446
 
6.4%
25.974
 
1.1%
ValueCountFrequency (%)
25.2526
 
7.5%
25.3446
 
6.4%
25.4583
 
8.3%
25.52853
40.6%
25.61112
 
15.8%
25.7851
 
12.1%
25.8577
 
8.2%
25.974
 
1.1%
ValueCountFrequency (%)
25.974
 
1.1%
25.8577
 
8.2%
25.7851
 
12.1%
25.61112
 
15.8%
25.52853
40.6%
25.4583
 
8.3%
25.3446
 
6.4%
25.2526
 
7.5%

T5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.0 KiB
25.5
5993 
25.6
1029 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters28088
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25.6
2nd row25.6
3rd row25.6
4th row25.6
5th row25.6

Common Values

ValueCountFrequency (%)
25.55993
85.3%
25.61029
 
14.7%

Length

2022-11-11T11:25:38.438438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:38.483406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
25.55993
85.3%
25.61029
 
14.7%

Most occurring characters

ValueCountFrequency (%)
513015
46.3%
27022
25.0%
.7022
25.0%
61029
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21066
75.0%
Other Punctuation7022
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
513015
61.8%
27022
33.3%
61029
 
4.9%
Other Punctuation
ValueCountFrequency (%)
.7022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
513015
46.3%
27022
25.0%
.7022
25.0%
61029
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII28088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
513015
46.3%
27022
25.0%
.7022
25.0%
61029
 
3.7%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.31073768
Minimum25.2
Maximum25.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.518579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.2
5-th percentile25.2
Q125.2
median25.2
Q325.3
95-th percentile25.8
Maximum25.8
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1711537376
Coefficient of variation (CV)0.006762099932
Kurtosis2.124647097
Mean25.31073768
Median Absolute Deviation (MAD)0
Skewness1.778626121
Sum177732
Variance0.02929360188
MonotonicityDecreasing
2022-11-11T11:25:38.557264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
25.23715
52.9%
25.31756
25.0%
25.4505
 
7.2%
25.8424
 
6.0%
25.6260
 
3.7%
25.5192
 
2.7%
25.7170
 
2.4%
ValueCountFrequency (%)
25.23715
52.9%
25.31756
25.0%
25.4505
 
7.2%
25.5192
 
2.7%
25.6260
 
3.7%
25.7170
 
2.4%
25.8424
 
6.0%
ValueCountFrequency (%)
25.8424
 
6.0%
25.7170
 
2.4%
25.6260
 
3.7%
25.5192
 
2.7%
25.4505
 
7.2%
25.31756
25.0%
25.23715
52.9%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.80168043
Minimum24.4
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.600123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile24.5
Q124.7
median24.8
Q324.9
95-th percentile25
Maximum25
Range0.6
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1468562315
Coefficient of variation (CV)0.005921221019
Kurtosis-0.06878050591
Mean24.80168043
Median Absolute Deviation (MAD)0.1
Skewness-0.7367026468
Sum174157.4
Variance0.02156675273
MonotonicityNot monotonic
2022-11-11T11:25:38.636302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
24.92230
31.8%
24.81719
24.5%
24.7977
13.9%
25928
13.2%
24.6652
 
9.3%
24.5377
 
5.4%
24.4139
 
2.0%
ValueCountFrequency (%)
24.4139
 
2.0%
24.5377
 
5.4%
24.6652
 
9.3%
24.7977
13.9%
24.81719
24.5%
24.92230
31.8%
25928
13.2%
ValueCountFrequency (%)
25928
13.2%
24.92230
31.8%
24.81719
24.5%
24.7977
13.9%
24.6652
 
9.3%
24.5377
 
5.4%
24.4139
 
2.0%

T8
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.0 KiB
25.1
3149 
25.0
3139 
24.9
713 
24.8
 
21

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters28088
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25.0
2nd row25.0
3rd row25.0
4th row25.0
5th row25.0

Common Values

ValueCountFrequency (%)
25.13149
44.8%
25.03139
44.7%
24.9713
 
10.2%
24.821
 
0.3%

Length

2022-11-11T11:25:38.681668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:38.729831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
25.13149
44.8%
25.03139
44.7%
24.9713
 
10.2%
24.821
 
0.3%

Most occurring characters

ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
56288
22.4%
13149
11.2%
03139
11.2%
4734
 
2.6%
9713
 
2.5%
821
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21066
75.0%
Other Punctuation7022
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27022
33.3%
56288
29.8%
13149
14.9%
03139
14.9%
4734
 
3.5%
9713
 
3.4%
821
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.7022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
56288
22.4%
13149
11.2%
03139
11.2%
4734
 
2.6%
9713
 
2.5%
821
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII28088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
56288
22.4%
13149
11.2%
03139
11.2%
4734
 
2.6%
9713
 
2.5%
821
 
0.1%

T9
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.0 KiB
24.9
3865 
24.8
2292 
25.0
822 
25.1
 
32
24.7
 
11

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters28088
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.8
2nd row24.8
3rd row24.8
4th row24.8
5th row24.8

Common Values

ValueCountFrequency (%)
24.93865
55.0%
24.82292
32.6%
25.0822
 
11.7%
25.132
 
0.5%
24.711
 
0.2%

Length

2022-11-11T11:25:38.774161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:38.823620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.93865
55.0%
24.82292
32.6%
25.0822
 
11.7%
25.132
 
0.5%
24.711
 
0.2%

Most occurring characters

ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
46168
22.0%
93865
13.8%
82292
 
8.2%
5854
 
3.0%
0822
 
2.9%
132
 
0.1%
711
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21066
75.0%
Other Punctuation7022
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27022
33.3%
46168
29.3%
93865
18.3%
82292
 
10.9%
5854
 
4.1%
0822
 
3.9%
132
 
0.2%
711
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.7022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
46168
22.0%
93865
13.8%
82292
 
8.2%
5854
 
3.0%
0822
 
2.9%
132
 
0.1%
711
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII28088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27022
25.0%
.7022
25.0%
46168
22.0%
93865
13.8%
82292
 
8.2%
5854
 
3.0%
0822
 
2.9%
132
 
0.1%
711
 
< 0.1%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.70639419
Minimum24.6
Maximum25.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.865744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.6
5-th percentile24.6
Q124.7
median24.7
Q324.7
95-th percentile24.8
Maximum25.2
Range0.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0633568184
Coefficient of variation (CV)0.002564389523
Kurtosis7.85794372
Mean24.70639419
Median Absolute Deviation (MAD)0
Skewness1.671947419
Sum173488.3
Variance0.004014086438
MonotonicityNot monotonic
2022-11-11T11:25:38.904849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24.74731
67.4%
24.6805
 
11.5%
24.8660
 
9.4%
24.7550
 
7.8%
24.9258
 
3.7%
25.29
 
0.1%
25.17
 
0.1%
24.91
 
< 0.1%
251
 
< 0.1%
ValueCountFrequency (%)
24.6805
 
11.5%
24.7550
 
7.8%
24.74731
67.4%
24.8660
 
9.4%
24.9258
 
3.7%
24.91
 
< 0.1%
251
 
< 0.1%
25.17
 
0.1%
25.29
 
0.1%
ValueCountFrequency (%)
25.29
 
0.1%
25.17
 
0.1%
251
 
< 0.1%
24.91
 
< 0.1%
24.9258
 
3.7%
24.8660
 
9.4%
24.74731
67.4%
24.7550
 
7.8%
24.6805
 
11.5%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.07480775
Minimum25.9
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:38.945084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.9
5-th percentile26
Q126
median26.1
Q326.1
95-th percentile26.2
Maximum26.4
Range0.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.08053153647
Coefficient of variation (CV)0.003088480546
Kurtosis1.217848419
Mean26.07480775
Median Absolute Deviation (MAD)0.1
Skewness0.8174238763
Sum183097.3
Variance0.006485328366
MonotonicityNot monotonic
2022-11-11T11:25:38.986243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
26.13481
49.6%
262599
37.0%
26.2471
 
6.7%
26.3314
 
4.5%
25.9148
 
2.1%
26.49
 
0.1%
ValueCountFrequency (%)
25.9148
 
2.1%
262599
37.0%
26.13481
49.6%
26.2471
 
6.7%
26.3314
 
4.5%
26.49
 
0.1%
ValueCountFrequency (%)
26.49
 
0.1%
26.3314
 
4.5%
26.2471
 
6.7%
26.13481
49.6%
262599
37.0%
25.9148
 
2.1%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.06193748
Minimum25.45
Maximum26.925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:39.036784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.45
5-th percentile25.7
Q125.9
median26.05
Q326.2
95-th percentile26.5
Maximum26.925
Range1.475
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2374379028
Coefficient of variation (CV)0.00911052384
Kurtosis0.009625113807
Mean26.06193748
Median Absolute Deviation (MAD)0.15
Skewness0.3365477436
Sum183006.925
Variance0.05637675766
MonotonicityNot monotonic
2022-11-11T11:25:39.092321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.1806
 
11.5%
26653
 
9.3%
25.9481
 
6.8%
26.05400
 
5.7%
26.2381
 
5.4%
25.95301
 
4.3%
25.8277
 
3.9%
26.3250
 
3.6%
25.7208
 
3.0%
26.4192
 
2.7%
Other values (46)3073
43.8%
ValueCountFrequency (%)
25.456
 
0.1%
25.4753
 
< 0.1%
25.52
 
< 0.1%
25.5258
 
0.1%
25.5559
0.8%
25.57533
0.5%
25.636
0.5%
25.62535
0.5%
25.6572
1.0%
25.67575
1.1%
ValueCountFrequency (%)
26.9256
 
0.1%
26.91
 
< 0.1%
26.8251
 
< 0.1%
26.751
 
< 0.1%
26.7257
 
0.1%
26.745
0.6%
26.6759
 
0.1%
26.651
 
< 0.1%
26.62517
 
0.2%
26.659
0.8%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct140
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.9727008
Minimum0
Maximum71.906
Zeros38
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size55.0 KiB
2022-11-11T11:25:39.213675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.375
Q132.906
median40.219
Q353.625
95-th percentile67.031
Maximum71.906
Range71.906
Interquartile range (IQR)20.719

Descriptive statistics

Standard deviation13.78551387
Coefficient of variation (CV)0.3207970087
Kurtosis-0.3726806342
Mean42.9727008
Median Absolute Deviation (MAD)9.75
Skewness0.05470203506
Sum301754.305
Variance190.0403927
MonotonicityNot monotonic
2022-11-11T11:25:39.270388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.688396
 
5.6%
34.125355
 
5.1%
36.563279
 
4.0%
32.906277
 
3.9%
30.469273
 
3.9%
54.844205
 
2.9%
39201
 
2.9%
43.875201
 
2.9%
40.219198
 
2.8%
46.313186
 
2.6%
Other values (130)4451
63.4%
ValueCountFrequency (%)
038
0.5%
3.0471
 
< 0.1%
6.70351
 
< 0.1%
8.53151
 
< 0.1%
9.14051
 
< 0.1%
9.758
 
0.1%
10.35956
 
0.1%
10.96927
0.4%
11.57859
 
0.1%
12.18751
 
< 0.1%
ValueCountFrequency (%)
71.90615
 
0.2%
71.29711
 
0.2%
70.688105
1.5%
70.07858
 
0.1%
69.46942
 
0.6%
68.85957
 
0.1%
68.2567
1.0%
67.640511
 
0.2%
67.03197
1.4%
66.42213
 
0.2%

Interactions

2022-11-11T11:25:37.002829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.061846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.666146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.203803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.772137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.402456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.972106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.590167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.203050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.831290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.376739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.052024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.112674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.712261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.255628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.822023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.454281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.021302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.639003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.251938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.881123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.425659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.097978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.159442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.756254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.305460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.934924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.504114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.068106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.685845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.299028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.927056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.473264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.149810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.209935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.806301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.358283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.986750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.556770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.120928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.736674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.349589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.977556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.525841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.201687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.260863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.857234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.411104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.039982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.609537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.236539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.791489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.401816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.027355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.578663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.253379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.312688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.907050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.464923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.093801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.661933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.287740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.845413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.454586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.078267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.631540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.303241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.362859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.954884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.517018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.145626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.715751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.337883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.949016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.506412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.128552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.683814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.353074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.413137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.006760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.567793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.196601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.767577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.387681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.998897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.631989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.179380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.734178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.402905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.463973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.057642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.618621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.248456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.819402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.438057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.055184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.681821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.228216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.785056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.451741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.511767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.106128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.668894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.298288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.869254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.487595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.103023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.731098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.276059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.834327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:37.503566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:31.616267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.155964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:32.721341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.350115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:33.921079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:34.539371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.152855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:35.781490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.326801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:36.951931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:25:39.328658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:25:39.402493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:25:39.480662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:25:39.559929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:25:39.633026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T11:25:39.689226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:25:37.588086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:25:37.694671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-11T11:25:37.739780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

TIME轉速T1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000024.925.025.125.225.625.825.025.024.824.626.126.0750.0
10.083333024.925.025.125.225.625.825.025.024.824.726.126.0500.0
20.166667024.925.025.125.225.625.825.025.024.824.726.126.0500.0
30.250000024.925.025.125.225.625.825.025.024.824.626.126.0500.0
40.333333024.925.025.125.225.625.825.025.024.824.726.126.0500.0
50.416667024.925.025.125.225.625.825.025.024.824.626.126.0500.0
60.500000024.925.025.125.225.625.825.025.024.824.726.126.0500.0
70.583333024.925.025.125.225.625.825.025.024.824.726.126.0500.0
80.666667024.925.025.125.225.625.825.025.024.924.726.126.0500.0
90.750000024.925.025.125.225.625.825.025.024.924.726.126.0500.0

Last rows

TIME轉速T1T2T3T4T5T6T7T8T9T10T11T12Z
7012584.333333026.125.024.925.925.625.224.625.125.024.926.126.30054.2345
7013584.416667026.225.025.025.925.625.224.625.124.924.926.126.32553.6250
7014584.500000026.225.025.025.925.625.224.625.125.024.926.026.42553.6250
7015584.583333026.225.025.025.925.625.224.725.124.924.926.026.52553.6250
7016584.666667026.225.025.025.925.625.224.725.124.924.926.026.55053.0155
7017584.750000026.225.025.025.925.625.224.725.124.924.926.026.55052.4060
7018584.833333026.325.125.025.925.625.224.725.124.924.926.126.55052.4060
7019584.916667026.325.125.025.925.625.224.725.124.924.926.126.55052.4060
7020585.000000026.325.125.025.925.625.224.725.125.024.926.126.57552.4060
7021585.083333026.325.125.025.925.625.224.725.125.024.926.126.60051.7970